following should work Sample DataFrame some_df = sc.parallelize([ (“A”, “no”), (“B”, “yes”), (“B”, “yes”), (“B”, “no”)] ).toDF([“user_id”, “phone_number”]) Converting DataFrame to Pandas DataFrame pandas_df = some_df.toPandas()
Spark >= 3.0.0 Since Spark 3.0.0 this can be done without using UDF. from pyspark.ml.functions import vector_to_array (df .withColumn(“xs”, vector_to_array(“vector”))) .select([“word”] + [col(“xs”)[i] for i in range(3)])) ## +——-+—–+—–+—–+ ## | word|xs[0]|xs[1]|xs[2]| ## +——-+—–+—–+—–+ ## | assert| 1.0| 2.0| 3.0| ## |require| 0.0| 2.0| 0.0| ## +——-+—–+—–+—–+ Spark < 3.0.0 One possible approach is to … Read more
Watch out: I believe the accepted answer is not quite right! I’m glad you ask this question, because the behavior of these similarly-named functions differs in important and unexpected ways that are not well documented in the official spark documentation. The first part of the accepted answer is correct: calling df.repartition(COL, numPartitions=k) will create a … Read more
There seems to be a related bug report: https://issues.apache.org/jira/browse/SPARK-10314 Since there seems to be a pull request for this, there might be a chance to soon get a fix for this. From this thread, https://groups.google.com/forum/#!topic/tachyon-users/xb8zwqIjIa4, it looks like Spark is using TRY_CACHE mode to write to Tachyon so the data seems to get lost when … Read more
I made this script, It worked for my 10 pandas Data frames from pyspark.sql.types import * # Auxiliar functions def equivalent_type(f): if f == ‘datetime64[ns]’: return TimestampType() elif f == ‘int64’: return LongType() elif f == ‘int32’: return IntegerType() elif f == ‘float64’: return DoubleType() elif f == ‘float32’: return FloatType() else: return StringType() def … Read more
Your logic condition is wrong. IIUC, what you want is: import pyspark.sql.functions as f df.filter((f.col(‘d’)<5))\ .filter( ((f.col(‘col1’) != f.col(‘col3’)) | (f.col(‘col2’) != f.col(‘col4’)) & (f.col(‘col1’) == f.col(‘col3’))) )\ .show() I broke the filter() step into 2 calls for readability, but you could equivalently do it in one line. Output: +—-+—-+—-+—-+—+ |col1|col2|col3|col4| d| +—-+—-+—-+—-+—+ | A| … Read more
The simple answer (from the Databricks FAQ on this matter) is to perform the join where the joined columns are expressed as an array of strings (or one string) instead of a predicate. Below is an example adapted from the Databricks FAQ but with two join columns in order to answer the original poster’s question. … Read more
This should work in Spark 1.6 or later: df.select(df.col(“data.*”)) or df.select(df.col(“data.id”), df.col(“data.keyNote”), df.col(“data.details”))
You can get the names from the schema by doing spark_df.schema.names Printing the schema can be useful to visualize it as well spark_df.printSchema()
This happens because Spark tries to do Broadcast Hash Join and one of the DataFrames is very large, so sending it consumes much time. You can: Set higher spark.sql.broadcastTimeout to increase timeout – spark.conf.set(“spark.sql.broadcastTimeout”, newValueForExample36000) persist() both DataFrames, then Spark will use Shuffle Join – reference from here PySpark In PySpark, you can set the … Read more